In this letter, a novel adaptive total variation (ATV) model is proposed for image inpainting. The classical TV model is a partial differential equation (PDE)-based technique. While the TV model can preserve the image edges well, it has some drawbacks, such as staircase effect in the inpainted image and slow convergence rate. By analyzing the diffusion mechanism of TV model and introducing a new edge detection operator named difference curvature, we propose a novel ATV inpainting model. The proposed ATV model can diffuse the image information smoothly and quickly, namely, this model not only eliminates the staircase effect but also accelerates the convergence rate. Experimental results demonstrate the effectiveness of the proposed scheme.

Blur is one of the most common distortion type and greatly impacts image quality. Most existing no-reference (NR) image blur metrics produce scores without a fixed range, so it is hard to judge the extent of blur directly. This letter presents a NR perceptual blur metric using Saliency Guided Gradient Similarity (SGGS), which produces blur scores with a fixed range of (0,1). A blurred image is first reblurred using a Gaussian low-pass filter, producing a heavily blurred image. With this reblurred image as reference, a local blur map is generated by computing the gradient similarity. Finally, visual saliency is employed in the pooling to adapt to the characteristics of the human visual system (HVS). The proposed metric features fixed range, fast computation and better consistency with the HVS. Experiments demonstrate its advantages.

This letter presents an objective quality index for benchmarking image inpainting algorithms. Under the guidance of the masks of damaged areas, the boundary region and the inpainting region are first located. Then, the statistical features are extracted from the boundary and inpainting regions respectively. For the boundary region, we utilize Weibull distribution to fit the gradient magnitude histograms of the exterior and interior regions around the boundary, and the Kullback-Leibler Divergence (KLD) is calculated to measure the boundary distortions caused by imperfect inpainting. Meanwhile, the quality of the inpainting region is measured by comparing the naturalness factors between the inpainted image and the reference image. Experimental results demonstrate that the proposed metric outperforms the relevant state-of-the-art quality metrics.

The quality assessment of screen content images (SCIs) has been attractive recently. Different from natural images, SCI is usually a mixture of picture and text. Traditional quality metrics are mainly designed for natural images, which do not fit well into the SCIs. Motivated by this, this letter presents a simple and effective method to naturalize SCIs, so that the traditional quality models can be applied for SCI quality prediction. Specifically, bicubic interpolation-based up-sampling is proposed to achieve this goal. Extensive experiments and comparisons demonstrate the effectiveness of the proposed method.

A new image watermarking scheme is presented to achieve high capacity information hiding and geometric invariance simultaneously. Visually salient region is introduced into watermark synchronization. The saliency value of a region is used as the quantitative measure of robustness, based on which the idea of locally most salient region (LMSR) is proposed to generate the disjoint invariant regions. A meaningful binary watermark is then encoded using Chinese Remainder Theorem (CRT) in transform domain. Simulation results and comparisons demonstrate the effectiveness of the proposed scheme.

This letter presents a new image quality metric using low order discrete orthogonal moments. The moment features are extracted in a block manner and the relative moment differences (RMD) are computed. A new exponential function based on RMD is proposed to generate the quality score. The performance of the proposed method is evaluated on public databases. Experimental results and comparisons demonstrate the efficiency of the proposed method.

Image retouching is fundamental in photography, which is widely used to improve the perceptual quality of a low-quality image. Traditional image quality metrics are designed for degraded images, so they are limited in evaluating the quality of retouched images. This letter presents a RETouched Image QUality Evaluation (RETIQUE) algorithm by measuring structure and color changes between the original and retouched images. Structure changes are measured by gradient similarity. Color colorfulness and saturation are utilized to measure color changes. The overall quality score of a retouched image is computed as the linear combination of gradient similarity and color similarity. The performance of RETIQUE is evaluated on a public Digitally Retouched Image Quality (DRIQ) database. Experimental results demonstrate that the proposed metric outperforms the state-of-the-arts.

This letter presents a new image watermarking scheme using Polar Sine Transform (PST), a new kind of orthogonal moment defined on a circular domain. The PSTs are easy to compute and have no numerical stability problem, thus are more suitable for watermarking. In the proposed method, the PSTs are modified according to the binary watermark bits, producing a compensation image. The watermarked image is obtained by adding the compensation image to the original image directly. Simulation results show the advantages of the proposed scheme in terms of both watermark capacity and watermark robustness.

Considering an uncertain multi-objective optimization system with interval coefficients, this letter proposes an interval multi-objective particle swarm optimization algorithm. In order to improve its performance, a crowding distance measure based on the distance and the overlap degree of intervals, and a method of updating the archive based on the acceptance coefficient of decision-maker, are employed. Finally, results show that our algorithm is capable of generating excellent approximation of the true Pareto front.

A quantized index for evaluating the pattern similarity of two different datasets is designed by calculating the number of correlated dictionary atoms. Guided by this theory, task-specific biometric recognition model transferred from state-of-the-art DNN models is realized for both face and vein recognition.

This letter presents a no-reference blocking artifact measure based on analysis of color discontinuities in YUV color space. Color shift and color disappearance are first analyzed in JPEG images. For color-shifting and color-disappearing areas, the blocking artifact scores are obtained by computing the gradient differences across the block boundaries in U component and Y component, respectively. An overall quality score is then produced as the average of the local ones. Extensive simulations and comparisons demonstrate the efficiency of the proposed method.

Depth-image-based-rendering (DIBR) is a popular technique for view synthesis. The rendering process mainly introduces artifacts around edges, which leads to degraded quality. This letter proposes a DIBR-synthesized image quality metric by measuring the Statistics of both Edge Intensity and Orientation (SEIO). The Canny operator is first used to detect edges. Then the gradient maps are calculated, based on which the intensity and orientation of the edge pixels are computed for both the reference and synthesized images. The distance between the two intensity histograms and that between the two orientation histograms are computed. Finally, the two distances are pooled to obtain the overall quality score. Experimental results demonstrate the advantages of the presented method.